{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Import Moduels\n",
    "\n",
    "## System Tools\n",
    "import os\n",
    "import numpy as np\n",
    "from collections.abc import Iterable\n",
    "#from collections import OrderedDict\n",
    "## I/O Tools\n",
    "import _pickle as pickle\n",
    "## Data Process Tools\n",
    "import pandas as pd\n",
    "import datetime\n",
    "## Graphs\n",
    "import matplotlib.pyplot as plt\n",
    "from Toolbox_Graph import Graph_PDF, IRF_SinglePlot\n",
    "#import matplotlib.backends.backend_pdf as figpdf\n",
    "## Statistical Tools\n",
    "import statsmodels.formula.api as sm\n",
    "## Database API\n",
    "# from fredapi import Fred\n",
    "## Numerical API\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.optimize import bisect\n",
    "## Multi-Level Index Slicer\n",
    "idx = pd.IndexSlice\n",
    "\n",
    "import math \n",
    "import re\n",
    "# End of Section: Import Moduels\n",
    "###############################################################################\n",
    "\n",
    "\n",
    "#%% Setup Working Directory\n",
    "\n",
    "# Change to the Project Folder\n",
    "FolderList = [\"C:\\\\XingGuo\\\\Research\\\\CFM\\\\\" ]\n",
    "for Folder in FolderList:\n",
    "    if os.path.exists(Folder):\n",
    "        os.chdir(Folder)    \n",
    "\n",
    "# End of Section: Setup Working Directory\n",
    "###############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Prepare for the Regression\n",
    "RegData = pickle.load(open(\"Data\\\\Data_ForRegression.p\",'rb'))\n",
    "MergedDS = RegData['DS']\n",
    "MS = RegData['MS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dis-Aggregate Level Evidence\n",
    "\n",
    "We run the regression \n",
    "$$\n",
    "\\ln c_{i,t+11}-\\ln c_{i,t-1}=\\texttt{Control}_{i,t}+\\beta\\cdot MS_{t}+\\gamma\\cdot \\mathbf{1}^{dom}_{i,t-1}\\cdot MS_{t}+\\varepsilon_{i,t}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Setup\n",
    "\n",
    "### Prepare the Sample\n",
    "\n",
    "## HH Data\n",
    "DataGap = 12\n",
    "GrowthVarList = ['Growth_'+xx for xx in ['Consumption','Consumption_NonDur','Consumption_Dur','Flag_PosDurConsumption','Income', \\\n",
    "                 'Asset_Total','Liability_Total','HhSize','OwnRent']]\n",
    "RegDS = MergedDS.dropna(subset=GrowthVarList)\n",
    "RegDS['Month'] = RegDS['period'].dt.month\n",
    "RegDS['Year'] = RegDS['period'].dt.year\n",
    "\n",
    "RegDS = RegDS[(RegDS['Year']>=2009) & (RegDS['Year']<=2015)].reset_index(drop=True)\n",
    "\n",
    "## MS\n",
    "TempMS = MS.sort_values('Date').reset_index(drop=True)\n",
    "TempMS['T_M'] = TempMS['Date'].apply(lambda x: x.year*12+x.month)\n",
    "TempMS = TempMS.set_index('T_M').sort_index()\n",
    "\n",
    "def TempFun_MS_1(T_M,Lag):\n",
    "    TempT = T_M-Lag\n",
    "    if TempT in TempMS.index.to_list():\n",
    "        return TempMS.loc[TempT,'MonetaryShocks']\n",
    "    else:\n",
    "        return np.nan\n",
    "def TempFun_MS_2(T_M_Start,T_M_Length):\n",
    "    TempInd = (TempMS.index<=T_M_Start+T_M_Length) & (TempMS.index>T_M_Start)\n",
    "    return TempMS.loc[TempInd,'MonetaryShocks'].sum()\n",
    "\n",
    "\n",
    "## Exposure to Monetary Shocks\n",
    "# Type 1:\n",
    "MaxLag = 24\n",
    "LagList = range(MaxLag+1)\n",
    "MSList_1 = ['MS_'+str(Lag) for Lag in LagList]\n",
    "for Lag in LagList:    \n",
    "    RegDS['MS_'+str(Lag)] = RegDS['T_M'].apply(lambda x: TempFun_MS_1(x+DataGap,Lag))\n",
    "# Type 2:\n",
    "RegDS['MS_TotalExposure'] = RegDS.apply(lambda x: TempFun_MS_2(x['T_M'],x['PeriodGap']), axis=1)\n",
    "\n",
    "\n",
    "\n",
    "## Tablulate the Key Variables of Interest\n",
    "ConsGrowthVarList = ['Growth_Consumption','Growth_Consumption_Dur','Growth_Consumption_NonDur']\n",
    "RegDS[ConsGrowthVarList].describe(percentiles=[0.01,0.05,0.1,0.25,0.5,0.75,0.9,0.95,0.99])\n",
    "## Winsorization\n",
    "TempVarList = ConsGrowthVarList+['Growth_Consumption','Growth_Consumption_Dur','Growth_Consumption_NonDur']\n",
    "for vv in TempVarList:\n",
    "    Cutoff = RegDS[vv].quantile([0.01,0.99]).values\n",
    "    TempInd = (RegDS[vv]<Cutoff[0]) | (RegDS[vv]>Cutoff[1])\n",
    "    RegDS.loc[TempInd,vv] = np.nan\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Regression \n",
    "\n",
    "## Functions\n",
    "def TempFun_Reg_1(DepVar,InDepVar):\n",
    "    RegDesign = DepVar+'~'+'+'.join(list(InDepVar.values()))\n",
    "    RegModel = sm.ols(formula=RegDesign,data=RegDS).fit()\n",
    "    TempCoef = RegModel.params\n",
    "\n",
    "    Temp_1 = TempCoef[[not re.findall(':(MS_\\d+)$',xx)==[] for xx in TempCoef.index]].rename('Dom-Exo')\n",
    "    Temp_1.index = [int(re.findall(':MS_(\\d+)$',xx)[0]) for xx in Temp_1.index]\n",
    "    Temp_2 = TempCoef[['MS_'+str(xx) for xx in LagList]].rename('Exo')\n",
    "    Temp_2.index = [int(re.findall('MS_(\\d+)',xx)[0]) for xx in Temp_2.index]\n",
    "    \n",
    "    return Temp_2,Temp_1,TempCoef,RegModel\n",
    "\n",
    "def TempFun_CumCoef(TempCoef,MaxLag=MaxLag,DataGap=DataGap):\n",
    "    CoefDict = {}\n",
    "    for ii in range(MaxLag+1):\n",
    "        if ii<DataGap:\n",
    "            CoefDict[ii] = TempCoef[ii]\n",
    "        else:\n",
    "            CoefDict[ii] = TempCoef[ii]+CoefDict[ii-12]\n",
    "    return pd.Series(CoefDict).rename('IRF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Local Projection:\n",
    "RegDS['MS_LP'] = RegDS['MS_'+str(DataGap-1)]\n",
    "RegDS['Future_Flag_PosDurConsumption_Int'] = np.nan\n",
    "RegDS.loc[RegDS['Future_Flag_PosDurConsumption']==True,'Future_Flag_PosDurConsumption_Int'] = 100\n",
    "RegDS.loc[RegDS['Future_Flag_PosDurConsumption']==False,'Future_Flag_PosDurConsumption_Int'] = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "InDepVar = {'MS': '+'.join(['C(Flag_ExoFin, Treatment(reference=True))','MS_LP']), \\\n",
    "            'Control':'+'.join(['C(AgeGroup)','C(Month)','Growth_Income']) \\\n",
    "            }\n",
    "DepVarList = ['Growth_Consumption','Growth_Consumption_NonDur','Growth_Consumption_Dur','Future_Flag_PosDurConsumption_Int']\n",
    "ResultDict = {}\n",
    "CoefDict = {}\n",
    "for DepVar in DepVarList:\n",
    "    RegDesign = DepVar+'~'+'+'.join(list(InDepVar.values()))\n",
    "    RegModel = sm.ols(formula=RegDesign,data=RegDS).fit()\n",
    "    TempCoef = RegModel.params\n",
    "    ResultDict[DepVar] = RegModel\n",
    "    CoefDict[DepVar] = pd.concat([RegModel.params,RegModel.tvalues],axis=1,keys=['coef','tvalue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                             MS_LP\nGrowth_Consumption                coef    0.300222\n                                  tvalue  0.176486\nGrowth_Consumption_NonDur         coef    1.790652\n                                  tvalue  1.429900\nGrowth_Consumption_Dur            coef   -1.114002\n                                  tvalue -1.403088\nFuture_Flag_PosDurConsumption_Int coef    2.153500\n                                  tvalue  1.219600",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>MS_LP</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Growth_Consumption</th>\n      <th>coef</th>\n      <td>0.300222</td>\n    </tr>\n    <tr>\n      <th>tvalue</th>\n      <td>0.176486</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Growth_Consumption_NonDur</th>\n      <th>coef</th>\n      <td>1.790652</td>\n    </tr>\n    <tr>\n      <th>tvalue</th>\n      <td>1.429900</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Growth_Consumption_Dur</th>\n      <th>coef</th>\n      <td>-1.114002</td>\n    </tr>\n    <tr>\n      <th>tvalue</th>\n      <td>-1.403088</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Future_Flag_PosDurConsumption_Int</th>\n      <th>coef</th>\n      <td>2.153500</td>\n    </tr>\n    <tr>\n      <th>tvalue</th>\n      <td>1.219600</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "Temp = pd.concat(CoefDict.values(),axis=1,keys=CoefDict.keys())\n",
    "Temp.loc[[not re.findall('(MS_LP)$',xx)==[] for xx in TempCoef.index],:].transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "InDepVar = {'MS': '+'.join(['C(Flag_ExoFin, Treatment(reference=True))*'+xx for xx in ['MS_LP']]), \\\n",
    "            'Control':'+'.join(['C(AgeGroup)','C(Month)','Growth_Income']) \\\n",
    "            }\n",
    "DepVarList = ['Growth_Consumption','Growth_Consumption_NonDur','Growth_Consumption_Dur','Future_Flag_PosDurConsumption_Int']\n",
    "ResultDict = {}\n",
    "CoefDict = {}\n",
    "for DepVar in DepVarList:\n",
    "    RegDesign = DepVar+'~'+'+'.join(list(InDepVar.values()))\n",
    "    RegModel = sm.ols(formula=RegDesign,data=RegDS).fit()\n",
    "    TempCoef = RegModel.params\n",
    "    ResultDict[DepVar] = RegModel\n",
    "    CoefDict[DepVar] = pd.concat([RegModel.params,RegModel.tvalues],axis=1,keys=['coef','tvalue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                             MS_LP  \\\nGrowth_Consumption                coef   -1.999974   \n                                  tvalue -0.519413   \nGrowth_Consumption_NonDur         coef   -1.600261   \n                                  tvalue -0.564494   \nGrowth_Consumption_Dur            coef   -0.757693   \n                                  tvalue -0.419149   \nFuture_Flag_PosDurConsumption_Int coef   -2.823423   \n                                  tvalue -0.701383   \n\n                                          C(Flag_ExoFin, Treatment(reference=True))[T.False]:MS_LP  \nGrowth_Consumption                coef                                             2.761256         \n                                  tvalue                                           0.665897         \nGrowth_Consumption_NonDur         coef                                             4.071605         \n                                  tvalue                                           1.333284         \nGrowth_Consumption_Dur            coef                                            -0.426816         \n                                  tvalue                                          -0.219403         \nFuture_Flag_PosDurConsumption_Int coef                                             5.960168         \n                                  tvalue                                           1.375753         ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>MS_LP</th>\n      <th>C(Flag_ExoFin, Treatment(reference=True))[T.False]:MS_LP</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Growth_Consumption</th>\n      <th>coef</th>\n      <td>-1.999974</td>\n      <td>2.761256</td>\n    </tr>\n    <tr>\n      <th>tvalue</th>\n      <td>-0.519413</td>\n      <td>0.665897</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Growth_Consumption_NonDur</th>\n      <th>coef</th>\n      <td>-1.600261</td>\n      <td>4.071605</td>\n    </tr>\n    <tr>\n      <th>tvalue</th>\n      <td>-0.564494</td>\n      <td>1.333284</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Growth_Consumption_Dur</th>\n      <th>coef</th>\n      <td>-0.757693</td>\n      <td>-0.426816</td>\n    </tr>\n    <tr>\n      <th>tvalue</th>\n      <td>-0.419149</td>\n      <td>-0.219403</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">Future_Flag_PosDurConsumption_Int</th>\n      <th>coef</th>\n      <td>-2.823423</td>\n      <td>5.960168</td>\n    </tr>\n    <tr>\n      <th>tvalue</th>\n      <td>-0.701383</td>\n      <td>1.375753</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "Temp = pd.concat(CoefDict.values(),axis=1,keys=CoefDict.keys())\n",
    "Temp.loc[[not re.findall('(MS_LP)$',xx)==[] for xx in TempCoef.index],:].transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aggregate Level Evidence\n",
    "\n",
    "Here, we compute the average consumption $ c_{t} $ and run following regression\n",
    "$$\n",
    "\\ln c_{t}-\\ln c_{t-1} = \\alpha+\\sum_{\\iota=1}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-49\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_4\">\r\n     <g id=\"line2d_4\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"137.749659\" xlink:href=\"#m277d6996fb\" y=\"224.64\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_4\">\r\n      <!-- 2012 -->\r\n      <g transform=\"translate(125.024659 239.238437)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-50\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_5\">\r\n     <g id=\"line2d_5\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"171.511003\" xlink:href=\"#m277d6996fb\" y=\"224.64\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_5\">\r\n      <!-- 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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#%% Setup\n",
    "\n",
    "### Prepare the Sample\n",
    "\n",
    "## HH Data\n",
    "VarList = ['Consumption','Consumption_NonDur','Consumption_Dur','Flag_PosDurConsumption','Income','Asset_Total','Liability_Total']\n",
    "RegDS = MergedDS.dropna(subset=VarList).groupby('period')[VarList].mean()\n",
    "np.log(RegDS[['Consumption','Consumption_NonDur','Consumption_Dur','Income','Asset_Total','Liability_Total']]).plot()\n",
    "RegDS = RegDS.reset_index()\n",
    "RegDS['Month'] = RegDS['period'].dt.month\n",
    "RegDS['Year'] = RegDS['period'].dt.year\n",
    "RegDS['T_M'] = RegDS['period'].apply(lambda x: x.year*12+x.month)\n",
    "RegDS = RegDS.set_index('T_M').sort_index()\n",
    "RegDS['LogDiff_Consumption_Dur'] = -np.log(RegDS['Consumption_Dur']).diff(-12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## MS\n",
    "TempMS = MS.sort_values('Date').reset_index(drop=True)\n",
    "TempMS['T_M'] = TempMS['Date'].apply(lambda x: x.year*12+x.month)\n",
    "TempMS = TempMS.set_index('T_M').sort_index()\n",
    "\n",
    "def TempFun_MS_1(T_M,Lag):\n",
    "    TempT = T_M-Lag\n",
    "    if TempT in TempMS.index.to_list():\n",
    "        return TempMS.loc[TempT,'MonetaryShocks']\n",
    "    else:\n",
    "        return np.nan\n",
    "def TempFun_MS_2(T_M_Start,T_M_Length):\n",
    "    TempInd = (TempMS.index<=T_M_Start+T_M_Length) & (TempMS.index>T_M_Start)\n",
    "    return TempMS.loc[TempInd,'MonetaryShocks'].sum()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "RegDS = RegDS.merge(right=TempMS.reset_index()[['T_M','MonetaryShocks']],how='left',left_on='T_M',right_on='T_M')\n",
    "RegDS['LagMS_1'] = RegDS['MonetaryShocks'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x27cd7b5ebc8>"
     },
     "metadata": {},
     "execution_count": 10
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "RegDS.plot(x='period',y='LogDiff_Consumption_Dur')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "RegDesign = 'LogDiff_Consumption_Dur'+'~'+'C(Month)+LagMS_1'\n",
    "RegModel = sm.ols(formula=RegDesign,data=RegDS[(RegDS['Year']>2009) & (RegDS['Year']<2017) ]).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}